Supplemental enhancement information messages for neural network based video post processing

ABSTRACT

A method, computer program, and computer system is provided for video post-processing using supplemental enhancement information (SEI) messages by determining whether a structure of a neural network and one or more parameters associated with the neural network are defined for a decoder and requesting data corresponding to the structure of the neural network and the one or more parameters based on the determination using one or more SEI messages. The requested structure and parameter data is received, and post processing is performed on video data based on the received structure and parameter data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional PatentApplication Nos. 62/822,758 (filed Mar. 22, 2019) and 62/870,369 (filedJul. 3, 2019), filed in the U.S. Patent and Trademark Office, which areincorporated herein by reference in their entirety.

BACKGROUND

This disclosure relates generally to field of computing, and moreparticularly to video encoding.

ITU-T VCEG (Q6/16) and ISO/IEC MPEG (JTC 1/SC 29/WG 11) published theH.265/HEVC (High Efficiency Video Coding) standard in 2013 (version 1),2014 (version 2), 2015 (version 3), and 2016 (version 4). Since thenthey have been studying the potential need for standardization of futurevideo coding technology with a compression capability that significantlyexceeds that of the HEVC standard (including its extensions). In October2017, they issued the Joint Call for Proposals on Video Compression withCapability beyond HEVC (CfP). By Feb. 15, 2018, a total of 22 CfPresponses on standard dynamic range (SDR), 12 CfP responses on highdynamic range (HDR), and 12 CfP responses on 360 video categories weresubmitted, respectively. In April 2018, all received CfP responses wereevaluated in the 122 MPEG/10th JVET (Joint Video Exploration Team—JointVideo Expert Team) meeting. With careful evaluation, JVET formallylaunched the standardization of next-generation video coding beyondHEVC, i.e., the so-called Versatile Video Coding (VVC). Meanwhile, theAudio Video coding Standard (AVS) of China is also in progress.

SUMMARY

Embodiments relate to a method, system, and computer readable medium forvideo post-processing using supplemental enhancement information (SEI)messages. According to one aspect, a method for video post-processingusing SEI messages is provided. The method may include determiningwhether a structure of a neural network and one or more parametersassociated with the neural network are defined for a decoder. Datacorresponding to the structure of the neural network and the one or moreparameters may be requested based on the determination, using one ormore SEI messages. The requested structure and parameter data may bereceived, and post-processing may be performed on video data based onthe received structure and parameter data.

According to another aspect, a computer system for video post-processingusing SEI messages is provided. The computer system may include one ormore processors, one or more computer-readable memories, one or morecomputer-readable tangible storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories, whereby the computer system is capable ofperforming a method. The method may include receiving determiningwhether a structure of a neural network and one or more parametersassociated with the neural network are defined for a decoder. Datacorresponding to the structure of the neural network and the one or moreparameters may be requested based on the determination, using one ormore SEI messages. The requested structure and parameter data may bereceived, and post-processing may be performed on video data based onthe received structure and parameter data.

According to yet another aspect, a computer readable medium for videopost-processing using SEI messages is provided. The computer readablemedium may include one or more computer-readable storage devices andprogram instructions stored on at least one of the one or more tangiblestorage devices, the program instructions executable by a processor. Theprogram instructions are executable by a processor for performing amethod that may accordingly include determining whether a structure of aneural network and one or more parameters associated with the neuralnetwork are defined for a decoder. Data corresponding to the structureof the neural network and the one or more parameters may be requestedbased on the determination, using one or more SEI messages. Therequested structure and parameter data may be received, andpost-processing may be performed on video data based on the receivedstructure and parameter data.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIGS. 2A and 2B are exemplary SEI messages according to at least oneembodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program for video post-processing using SEI messages, according to atleast one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of computing, and moreparticularly to video encoding. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, use SEI messages for video encoding and decoding.Therefore, some embodiments have the capacity to improve the field ofcomputing by allowing for the use of neural networks to perform videoencoding and for SEI messages to provide the necessary parameters andinformation regarding the structure of the neural network.

As previously described, ITU-T VCEG (Q6/16) and ISO/IEC MPEG (JTC 1/SC29/WG 11) published the H.265/HEVC (High Efficiency Video Coding)standard in 2013 (version 1), 2014 (version 2), 2015 (version 3), and2016 (version 4). Since then they have been studying the potential needfor standardization of future video coding technology with a compressioncapability that significantly exceeds that of the HEVC standard(including its extensions). In October 2017, they issued the Joint Callfor Proposals on Video Compression with Capability beyond HEVC (CfP). ByFeb. 15, 2018, a total of 22 CfP responses on standard dynamic range(SDR), 12 CfP responses on high dynamic range (HDR), and 12 CfPresponses on 360 video categories were submitted, respectively. In April2018, all received CfP responses were evaluated in the 122 MPEG/10thJVET (Joint Video Exploration Team—Joint Video Expert Team) meeting.With careful evaluation, JVET formally launched the standardization ofnext-generation video coding beyond HEVC, i.e., the so-called VersatileVideo Coding (VVC). Meanwhile, the Audio Video coding Standard (AVS) ofChina is also in progress.

However, considering the complexity of neural network-based codingmethods, the normal codecs may not be able to perform the filteringprocess. It may be advantageous, therefore, for the neural networkfilter to be treated as a kind of post-processing filter, such that adetermination may be made by the display side as to whether or not touse the filter. To transmit the information of the neural networkfilter, a set of SEI messages may be defined, and if the display sidecannot process the neural network-based filter, the information relatedto neural network may be discarded, and the process may be skipped.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program for video post-processing using SEI messages.Referring now to FIG. 1, a functional block diagram of a networkedcomputer environment illustrating a GPS enhancement system 100(hereinafter “system”) for video post-processing using SEI messages. Itshould be appreciated that FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 4 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 5 and 6. The server computer 114 may also be located ina cloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for enhancing GPS locationdata is enabled to run a Video Encoding/Decoding Program 116(hereinafter “program”) that may interact with a database 112. The VideoEncoding/Decoding Program method is explained in more detail below withrespect to FIG. 3. In one embodiment, the computer 102 may operate as aninput device including a user interface while the program 116 may runprimarily on server computer 114. In an alternative embodiment, theprogram 116 may run primarily on one or more computers 102 while theserver computer 114 may be used for processing and storage of data usedby the program 116. It should be noted that the program 116 may be astandalone program or may be integrated into a larger videoencoding/decoding program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1. Furthermore, two or more devices shown in FIG. 1 may beimplemented within a single device, or a single device shown in FIG. 1may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIGS. 2A and 2B, exemplary SEI messages 200A and 200Bare depicted according to one or more embodiments. The SEI messages 200Aand 200B may include, among other things, parameters that mayaccordingly include:

nn_based_tools_type=0 may specify that a neural network post filter maybe used. The values 1, 2, and 3 may be reserved. nn_based_tools_type=0may specify nn_based_post_filter=1; otherwise, nn_based_post_filter=0.

predifined_nn_filter_flag=0 may specify that a neural network filter isnot predefined on the display side and that the parameters may be loadedfrom the stream. predefined_nn_filter_flag=1 may specify that the neuralnetwork filter may be predefined on the display side.

nn_model_id may specify an index of the predefined neural networkstructure that may be used.

predefined_nn_parameters may specify whether the neural networkparameters may been pre-defined. When predefined_nn_parameters=0,parameter_not_predefined_nn_parameter may be true.

input_packing_type may specify a type of the packing stage.input_packing_type=0 may mean six input channels of the CNN may begenerated as four luma blocks of reconstructed luma samples and twochroma blocks of reconstructed chroma samples. The four luma blocks maybe generated by 2×2 subsampling of the luma CTB four times withdifferent phases to form four quarter-size luma blocks.

w_para_quant_precision may specify the bit-depth of weight parameters.

s_para_quant_precision may specify the bit-depth of scale, shift, biasand other parameters except weight which bit-depth may be defined inw_para_quant_precision.

chroma_luma_seperate_flag may specify whether chroma component and lumacomponent may share the same neural network and processed together ornot. If chroma and luma share the same neural network,chroma_luma_seperate_flag=0 and comp_seperate_num=1; otherwise,chroma_luma_seperate_flag=1 and comp_seperate_num=2.

layer_num, sub_layer_filter_kernel_h, sub_layer_filter_kernel_v,sub_layer_input_channel, sub_layer_output_channel,sub_layer_bn_operation_flag, sub_layer_relu_operation_flag may be theparameters for each layer.

residual_add_flag=1 may specify there may be an adding operation to addthe original input and the output of the neural network for finalreconstruction.

boundary_weight_type=0 may specify the final reconstruction of neuralnetwork may be directly used without weight operation.boundary_weight_type=1 may specify that the reconstruction may be usedby a weight mask related to the CU boundary.

nn_para_w, and nn_para_s may be parameters of weights and scale, shift,bias (parameters except weights) with a pre-defined bit depth.

min_block_size may be a minimum block size for supporting whether neuralnetwork tools may be on or off. If the block size is N×N,min_block_size=N. The block may be square.

total_num_block may specify the total number of the minimal blocks thatmay support neural network tools being on or off in a tile group.

nn_filter_chose_set may specify that for each block, whether the neuralnetwork filter may be used and which set should be used.nn_filter_chose_set=0 may specify that the neural network filter may beoff for the this block. nn_filter_chose_set=1 may specify the neuralnetwork filter may be on, and the parameter set may be 1.nn_filter_chose_set=2 may specify that the neural network filter is onwith the, and the parameter set may be 2. The maximum number ofnn_filter_chose_set may be num_nn_para_set.

According to one embodiment, if the neural network structure andparameters are not pre-defined on the decoder side, the structure andparameter data of a neural network based filter may be transmitted in abitstream. According to an alternative embodiment, the decoder ordisplay side may have a pre-defined neural network structure, and thedetailed information of the neural network based filter may not need tobe transmitted in the bitstream. In SEI, only the index of which neuralnetwork structure is chosen may be transmitted.

In either case, if the display side chooses to process the neuralnetwork based tools, the display may use the SEI messages or the indexto obtain the parameters for neural network based processing. If thedisplay side chooses not to process the NN based tools, the SEI messagemay be discarded. The SEI messages may include, for example, anidentifier indicating a type of the neural network (NN) based tools(e.g., a neural network based post-processing filter), an identifier orindex indicating which kind of packing type or neural network model setshould be used, information as to whether the transmitted parameters arecompressed, the bit-depth precision values and descriptions of theparameters, a flag indicating whether luma data and chroma data usedifferent neural networks, an identifier in SEI shows the neural networkparameters are pre-defined, and detailed information of the neuralnetwork (e.g., the total layer numbers, the convolutional kernel size,the network structure, whether using special operation for each layer,using residual learning flag). The SEI messages may also include anidentifier indicating whether side information (e.g., block partitioninformation) that can be used for a weight mask in the filter process isused, a block-level supporting control size value that may be used totoggle NN on or off and to switch parameter sets at the block level, atotal number of blocks defined that can also be inferred from the blocksize and a frame/slice/tile size.

According to SEI information, the process of neural network filter mayinclude the convolution operation. A filter in each convolutional layer(e.g., M*M*N, will use a M*M convolutional filter) may be used togenerate an N-channel output, and the output of this layer may be usedas the input of the next layer. There may also exist a sum orconcatenation operation in the middle layer. The final output may be areconstruction after this post-filter processing occurs.

Referring now to FIG. 3, an operational flowchart 300 illustrating thesteps carried out by a program for encoding and decoding video using SEImessages is depicted. FIG. 3 may be described with the aid of FIGS. 1,2A, and 2B. As previously described, the Video Encoding/Decoding Program116 (FIG. 1) may quickly and effectively encode and decode video usingSEI messages.

At 302, a determination is made as to whether a structure of a neuralnetwork and one or more parameters associated with the neural networkare defined for a decoder. The neural network may be of size M*M*N andmay use M*M convolutional filters to generate an N-channel output. Inoperation, the Video Encoding/Decoding Program 116 (FIG. 1) maydetermine whether the structure of the neural network and the parametersassociated with the neural network are defined. For example, thestructure and parameter data may be stored in the database 112 (FIG. 1)on the server computer 114 (FIG. 1).

At 304, data corresponding to the structure of the neural network andthe one or more parameters is requested based on the determination usingone or more SEI messages. If the structure and parameters are defined,an index to the structure and parameters may be received. Conversely, ifthe structure and parameters are undefined, an identifier of thestructure and parameters may need to be received over the bitstream. Inoperation, the Video Encoding/Decoding Program 116 (FIG. 1) may requestdata corresponding to the structure and the parameters from the database112 (FIG. 1) or the data storage device 106 (FIG. 1) on the computer 102(FIG. 1) using SEI messages 200A (FIG. 2A) or 200B (FIG. 2B).

At 306, the requested structure and parameter data is received. Therequested data may include a total number of layers in the neuralnetwork, a size value associated with a convolutional kernel of theneural network, one or more special operations for each of the layers ofthe neural network, and a residual learning flag associated with theneural network, a flag indicating whether luma data and chroma dataassociated with the video data use different neural networks, anidentifier indicating a total number of neural network parameter sets,one or more bit-depth precision values and bit-depth precisiondescription identifiers for the parameter data, a determination ofwhether the transmitted parameters are compressed and quantized, anidentifier indicating whether side information is used, and ablock-level supporting control size value. In operation, the VideoEncoding/Decoding Program 116 (FIG. 1) may receive the requestedinformation from the database 112 (FIG. 1) or from the data storagedevice 106 (FIG. 1) over the communication network 110 (FIG. 1).

At 308, post processing is performed on video data based on the receivedstructure and parameter data. For example, the structure and parameterdata may be parsed to determine characteristics associated with theencoding and decoding of the video data. In operation, the VideoEncoding/Decoding Program 116 (FIG. 1) may perform post-processing onthe video data using the data received from the database 112 (FIG. 1)corresponding to the data in the SEI messages 200A (FIG. 2A) and 200B(FIG. 2B). The Video Encoding/Decoding Program 116 (FIG. 1) mayoptionally transmit the processed video to the software program 108(FIG. 1) on the computer 102 (FIG. 1) over the communication network 110(FIG. 1) for display by the software program 108.

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 4 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 4. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1)and the Video Encoding/Decoding Program 116 (FIG. 1) on server computer114 (FIG. 1) are stored on one or more of the respectivecomputer-readable tangible storage devices 830 for execution by one ormore of the respective processors 820 via one or more of the respectiveRAMs 822 (which typically include cache memory). In the embodimentillustrated in FIG. 4, each of the computer-readable tangible storagedevices 830 is a magnetic disk storage device of an internal hard drive.Alternatively, each of the computer-readable tangible storage devices830 is a semiconductor storage device such as ROM 824, EPROM, flashmemory, an optical disk, a magneto-optic disk, a solid state disk, acompact disc (CD), a digital versatile disc (DVD), a floppy disk, acartridge, a magnetic tape, and/or another type of non-transitorycomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the Video Encoding/Decoding Program 116(FIG. 1) can be stored on one or more of the respective portablecomputer-readable tangible storage devices 936, read via the respectiveR/W drive or interface 832 and loaded into the respective hard drive830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1) and theVideo Encoding/Decoding Program 116 (FIG. 1) on the server computer 114(FIG. 1) can be downloaded to the computer 102 (FIG. 1) and servercomputer 114 from an external computer via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 836. From the network adaptersor interfaces 836, the software program 108 and the VideoEncoding/Decoding Program 116 on the server computer 114 are loaded intothe respective hard drive 830. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 6, a set of functional abstraction layers 600 providedby cloud computing environment 500 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Video Encoding/Decoding 96. VideoEncoding/Decoding 96 may encode and decode video using SEI messages.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of video post-processing usingsupplemental enhancement information (SEI) messages, the methodcomprising: requesting data corresponding to a structure of a neuralnetwork and data corresponding to one or more parameters based on one ormore SEI messages, wherein one of the one or more parameters include aninput packing type corresponding to one or more luma blocks ofreconstructed luma samples and one or more chroma blocks ofreconstructed chroma samples, wherein based on the structure of theneural network and the one or more parameters being pre-defined for adecoder, the data comprises an identifier corresponding to the structuredata and parameter data, and wherein based on the structure of theneural network and the one or more parameters not being pre-defined forthe decoder, the structure data and parameter data are received from abitstream; receiving the requested structure data and parameter data;and performing post-processing of video data based on the receivedstructure data and parameter data.
 2. The method of claim 1, wherein therequested data comprises an index indicating whether a predefined neuralnetwork structure is used.
 3. The method of claim 1, wherein therequested data comprises a total number of layers in the neural network,a size value associated with a convolutional kernel of the neuralnetwork, one or more special operations for each of the layers of theneural network, and a residual learning flag associated with the neuralnetwork.
 4. The method of claim 1, wherein the requested data comprisesa flag indicating whether luma data and chroma data associated with thevideo data use different neural networks.
 5. The method of claim 1,wherein the requested data comprises an identifier indicating a totalnumber of neural network parameter sets.
 6. The method of claim 1,wherein the requested data comprises one or more bit-depth precisionvalues and bit-depth precision description identifiers for the parameterdata.
 7. The method of claim 1, wherein the requested data comprises adetermination of whether the transmitted parameters are compressed andquantized.
 8. The method of claim 1, wherein the requested datacomprises an identifier indicating whether side information is used,wherein the side information is used to calculate a weight mask in thefilter process.
 9. The method of claim 1, wherein the requested datacomprises a block-level supporting control size value, wherein theblock-level support control size value is used to toggle use of one ormore neural network tools and parameter set switching at the blocklevel.
 10. A computer system for video post-processing usingsupplemental enhancement information (SEI) messages, the computer systemcomprising: one or more computer-readable non-transitory storage mediaconfigured to store computer program code; and one or more computerprocessors configured to access said computer program code and operateas instructed by said computer program code, said computer program codeincluding: requesting code configured to cause the one or more computerprocessors to request data corresponding to a structure of a neuralnetwork and data corresponding to one or more parameters based on one ormore SEI messages, wherein one of the one or more parameters include aninput packing type corresponding to one or more luma blocks ofreconstructed luma samples and one or more chroma blocks ofreconstructed chroma samples, wherein based on the structure of theneural network and the one or more parameters being pre-defined for adecoder, the data comprises an identifier corresponding to the structuredata and parameter data, and wherein based on the structure of theneural network and the one or more parameters not being pre-defined forthe decoder, the structure data and parameter data are received from abitstream; receiving code configured to cause the one or more computerprocessors to receive the requested structure data and parameter data;and performing code configured to cause the one or more computerprocessors to perform post-processing of video data based on thereceived structure data and parameter data.
 11. The computer system ofclaim 10, wherein the requested data comprises an index indicatingwhether a predefined neural network structure is used.
 12. The computersystem of claim 10, wherein the requested data comprises a total numberof layers in the neural network, a size value associated with aconvolutional kernel of the neural network, one or more specialoperations for each of the layers of the neural network, and a residuallearning flag associated with the neural network.
 13. The computersystem of claim 10, wherein the requested data comprises a flagindicating whether luma data and chroma data associated with the videodata use different neural networks.
 14. The computer system of claim 10,wherein the requested data comprises an identifier indicating a totalnumber of neural network parameter sets.
 15. The computer system ofclaim 10, wherein the requested data comprises one or more bit-depthprecision values and bit-depth precision description identifiers for theparameter data.
 16. The computer system of claim 10, wherein therequested data comprises an identifier indicating whether sideinformation is used, wherein the side information is used to calculate aweight mask in the filter process.
 17. The computer system of claim 10,wherein the requested data comprises a block-level supporting controlsize value, wherein the block-level support control size value is usedto toggle use of one or more neural network tools and parameter setswitching at the block level.
 18. A non-transitory computer readablemedium having stored thereon a computer program for videopost-processing using supplemental enhancement information (SEI)messages, the computer program configured to cause one or more computerprocessors to: request data corresponding to a structure of a neuralnetwork and data corresponding to one or more parameters based on one ormore SEI messages, wherein one of the one or more parameters include aninput packing type corresponding to one or more luma blocks ofreconstructed luma samples and one or more chroma blocks ofreconstructed chroma samples, wherein based on the structure of theneural network and the one or more parameters being pre-defined for adecoder, the data comprises an identifier corresponding to the structuredata and parameter data, and wherein based on the structure of theneural network and the one or more parameters not being pre-defined forthe decoder, the structure data and parameter data are received from abitstream; receive the requested structure data and parameter data; andperform post-processing on video data based on the received structuredata and parameter data.